I build Zooq, an API and MCP server that gives AI agents structured access to LinkedIn data without anyone connecting their own account. Most of what I know about GTM came from watching agents run these plays badly first: lists that could not be explained, personalization that smelled synthetic, account briefs that were reformatted About pages. The skills I contribute here encode the guardrails that fixed those failures, written so they hold up whatever data source sits underneath.
4 skills · 0 installs
Amir Baldiga
Founder, Zooq
Use this skill before a first meeting, an ABM push, or a territory review, when you need a defensible account brief instead of a copied About page. Produces firmographics, positioning pillars, an evidence-tagged SWOT, a named exec list, and live hiring signals, with every claim marked as sourced or inferred.
Amir Baldiga
Founder, Zooq
Use this skill when you have a handful of accounts or people who converted and need more like them, or when a black-box 'similar profiles' feed gives you a list you cannot explain. Extracts an explicit similarity signature from the seed and searches on it, so you control what 'similar' means and can widen or tighten deliberately.
Amir Baldiga
Founder, Zooq
Use this skill when someone claims AI personalization lifts reply rates, or before you let an agent write outbound at scale. Runs a control/personalized/holdout split on your connection list, enforces a safety gate on every generated message, and returns a defensible read on whether personalization actually beat the generic baseline.
Amir Baldiga
Founder, Zooq
Use this skill when a role worked and you want more of that person, or when you are backfilling someone strong and a job-board post is not going to find them. Takes one exemplar profile and returns a ranked, scored shortlist with the reasoning behind each score visible.